- Plural of eigenvector
In mathematics, a vector may be thought of as an arrow. It has a length, called its magnitude, and it points in some particular direction. A linear transformation may be considered to operate on a vector to change it, usually changing both its magnitude and its direction. An eigenvector of a given linear transformation is a non-zero vector which is multiplied by a constant called the as a result of that transformation. The direction of the eigenvector is either unchanged by that transformation (for positive eigenvalues) or reversed (for negative eigenvalues).
For example, an eigenvalue of +2 means that the eigenvector is doubled in length and points in the same direction. An eigenvalue of +1 means that the eigenvector is unchanged, while an eigenvalue of −1 means that the eigenvector is reversed in direction. An eigenspace of a given transformation is the span of the eigenvectors of that transformation with the same eigenvalue, together with the zero vector (which has no direction). An eigenspace is an example of a subspace of a vector space.
In linear algebra, every linear transformation between finite-dimensional vector spaces can be given by a matrix, which is a rectangular array of numbers arranged in rows and columns. Standard methods for finding eigenvalues, eigenvectors, and eigenspaces of a given matrix are discussed below.
These concepts play a major role in several branches of both pure and applied mathematics — appearing prominently in linear algebra, functional analysis, and to a lesser extent in nonlinear mathematics.
Many kinds of mathematical objects can be treated as vectors: functions, harmonic modes, quantum states, and frequencies, for example. In these cases, the concept of direction loses its ordinary meaning, and is given an abstract definition. Even so, if this abstract direction is unchanged by a given linear transformation, the prefix "eigen" is used, as in eigenfunction, eigenmode, eigenstate, and eigenfrequency.
Eigenvalues are often introduced in the context of linear algebra or matrix theory. Historically, however, they arose in the study of quadratic forms and differential equations.
Euler had also studied the rotational motion of a rigid body and discovered the importance of the principal axes. As Lagrange realized, the principal axes are the eigenvectors of the inertia matrix. In the early 19th century, Cauchy saw how their work could be used to classify the quadric surfaces, and generalized it to arbitrary dimensions. Cauchy also coined the term racine caractéristique (characteristic root) for what is now called eigenvalue; his term survives in characteristic equation.
Fourier used the work of Laplace and Lagrange to solve the heat equation by separation of variables in his famous 1822 book Théorie analytique de la chaleur. Sturm developed Fourier's ideas further and he brought them to the attention of Cauchy, who combined them with his own ideas and arrived at the fact that symmetric matrices have real eigenvalues. Schwarz studied the first eigenvalue of Laplace's equation on general domains towards the end of the 19th century, while Poincaré studied Poisson's equation a few years later.
At the start of the 20th century, Hilbert studied the eigenvalues of integral operators by viewing the operators as infinite matrices. He was the first to use the German word eigen to denote eigenvalues and eigenvectors in 1904, though he may have been following a related usage by Helmholtz. "Eigen" can be translated as "own", "peculiar to", "characteristic", or "individual" — emphasizing how important eigenvalues are to defining the unique nature of a specific transformation. For some time, the standard term in English was "proper value", but the more distinctive term "eigenvalue" is standard today.
The first numerical algorithm for computing eigenvalues and eigenvectors appeared in 1929, when Von Mises published the power method. One of the most popular methods today, the QR algorithm, was proposed independently by Francis and Kublanovskaya in 1961.
Definitions: the eigenvalue equationsee also Eigenplane
Linear transformations of a vector space, such as rotation, reflection, stretching, compression, shear or any combination of these, may be visualized by the effect they produce on vectors. In other words, they are vector functions. More formally, in a vector space L a vector function A is defined if for each vector x of L there corresponds a unique vector y = A(x) of L. For the sake of brevity, the parentheses around the vector on which the transformation is acting are often omitted. A vector function A is linear if it has the following two properties: scalar. Such a function is variously called a linear transformation, linear operator, or linear endomorphism on the space L. The key equation in this definition is the eigenvalue equation, Ax = λx. Most vectors x will not satisfy such an equation. A typical vector x changes direction when acted on by A, so that Ax is not a multiple of x. This means that only certain special vectors x are eigenvectors, and only certain special numbers λ are eigenvalues. Of course, if A is a multiple of the identity matrix, then no vector changes direction, and all non-zero vectors are eigenvectors.
The requirement that the eigenvector be non-zero is imposed because the equation A0 = λ0 holds for every A and every λ. Since the equation is always trivially true, it is not an interesting case. In contrast, an eigenvalue can be zero in a nontrivial way. Each eigenvector is associated with a specific eigenvalue. One eigenvalue can be associated with several or even with infinite number of eigenvectors.
Geometrically (Fig. 2), the eigenvalue equation means that under the transformation A eigenvectors experience only changes in magnitude and sign — the direction of Ax is the same as that of x. The eigenvalue λ is simply the amount of "stretch" or "shrink" to which a vector is subjected when transformed by A. If λ = 1, the vector remains unchanged (unaffected by the transformation). A transformation I under which a vector x remains unchanged, Ix = x, is defined as identity transformation. If λ = –1, the vector flips to the opposite direction (rotates to 180°); this is defined as reflection.
If x is an eigenvector of the linear transformation A with eigenvalue λ, then any scalar multiple αx is also an eigenvector of A with the same eigenvalue. Similarly if more than one eigenvector share the same eigenvalue λ, any linear combination of these eigenvectors will itself be an eigenvector with eigenvalue λ. . Together with the zero vector, the eigenvectors of A with the same eigenvalue form a linear subspace of the vector space called an eigenspace.
The eigenvectors corresponding to different eigenvalues are linearly independent meaning, in particular, that in an n-dimensional space the linear transformation A cannot have more than n eigenvectors with different eigenvalues.
If a basis is defined in vector space, all vectors can be expressed in terms of components. For finite dimensional vector spaces with dimension n, linear transformations can be represented with n × n square matrices. Conversely, every such square matrix corresponds a linear transformation for a given basis. Thus, in a the two-dimensional vector space R2 fitted with standard basis, the eigenvector equation for a linear transformation A can be written in the following matrix representation:
- \begin a_ & a_ \\ a_ & a_ \end \begin x \\ y \end = \lambda \begin x \\ y \end,
where the juxtaposition of matrices means matrix multiplication.
Characteristic equationWhen the transformation is represented by a square matrix, the eigenvalue equation can be expressed as
- A \mathbf - \lambda I \mathbf = \mathbf.
- \det(A - \lambda I) = 0.
This equation is defined as the characteristic equation (less often, secular equation) of A, and the left-hand side is defined as the characteristic polynomial. When expanded, this gives a polynomial equation for \lambda. The eigenvector x or its components are not present in the characteristic equation.
- \begin 2 & 1\\1 & 2 \end
- \det\begin 2-\lambda & 1\\1 & 2-\lambda \end = (2-\lambda)^2 - 1 = 0.
- \begin 2 & 1\\1 & 2 \end\beginx\\y\end = 3\times\beginx\\y\end.
The complexity of the problem for finding roots/eigenvalues of the characteristic polynomial increases rapidly with increasing the degree of the polynomial (the dimension of the vector space). There are exact solutions for dimensions below 5, but for higher dimensions there are no exact solutions and one has to resort to numerical methods to find them approximately. For large symmetric sparse matrices, Lanczos algorithm is used to compute eigenvalues and eigenvectors.
Existence and Multiplicity of Eigenvalues
For transformations on real vector spaces, the coefficients of the characteristic polynomial are all real. However, the roots are not necessarily real; they may well be complex numbers, or a mixture of real and complex numbers. For example, a matrix representing a planar rotation of 45 degrees will not leave any non-zero vector pointing in the the same direction. Over a complex vector space, the fundamental theorem of algebra guarantees that the characteristic polynomial has at least one root, and thus the linear transformation has at least one eigenvalue.
As well as distinct roots, the characteristic equation may also have repeated roots. However, having repeated roots does not imply there are multiple distinct (i.e. linearly independent) eigenvectors with that eigenvalue. The algebraic multiplicity of an eigenvalue is defined as the multiplicity of the corresponding root of the characteristic polynomial. The geometric multiplicity of an eigenvalue is defined as the dimension of the associated eigenspace, i.e. number of linearly independent eigenvectors with that eigenvalue.
Over a complex space, the sum of the algebraic multiplicities will equal the dimension of the vector space, but the sum of the geometric multiplicities may be smaller. In a sense, then it is possible that there may not be sufficient eigenvectors to span the entire space. This is intimately related to the question of whether a given matrix may be diagonalized by a suitable choice of coordinates.
Example: ShearShear in the plane is a transformation in which all points along a given line remain fixed while other points are shifted parallel to that line by a distance proportional to their perpendicular distance from the line. Shearing a plane figure does not change its area. Shear can be horizontal − along the X axis, or vertical − along the Y axis. In horizontal shear (see figure), a point P of the plane moves parallel to the X axis to the place P' so that its coordinate y does not change while the x coordinate increments to become x' = x + k y, where k is called the shear factor.
The matrix of a horizontal shear transformation is \begin1 & k\\ 0 & 1\end. The characteristic equation is λ2 − 2 λ + 1 = (1 − λ)2 = 0 which has a single, repeated root λ = 1. Therefore, the eigenvalue λ = 1 has algebraic multiplicity 2. The eigenvector(s) are found as solutions of
- \begin1 - 1 & k\\ 0 & 1 - 1 \end\beginx\\ y\end = \begin0 & k\\ 0 & 0 \end\beginx\\ y\end = ky = 0.
Example: Uniform scaling and Reflection
As a one-dimensional vector space, consider a rubber string tied to unmoving support in one end, such as that on a child's sling. Pulling the string away from the point of attachment stretches it and elongates it by some scaling factor λ which is a real number. Each vector on the string is stretched equally, with the same scaling factor λ, and although elongated it preserves its original direction. For a two-dimensional vector space, consider a rubber sheet stretched equally in all directions such as a small area of the surface of an inflating balloon (Fig. 3). All vectors originating at the fixed point on the balloon surface (the origin) are stretched equally with the same scaling factor λ. This transformation in two-dimensions is described by the 2×2 square matrix:
- A \mathbf = \begin\lambda & 0\\0 & \lambda\end \begin x \\ y \end = \begin\lambda \cdot x + 0 \cdot y \\0 \cdot x + \lambda \cdot y\end = \lambda \begin x \\ y \end = \lambda \mathbf.
Expressed in words, the transformation is equivalent to multiplying the length of any vector by λ while preserving its original direction. Since the vector taken was arbitrary, every non-zero vector in the vector space is an eigenvector. Whether the transformation is stretching (elongation, extension, inflation), or shrinking (compression, deflation) depends on the scaling factor: if λ > 1, it is stretching, if λ < 1, it is shrinking. Negative values of λ correspond to a reversal of direction, followed by a stretch or a shrink, depending on the absolute value of λ.
Example: Unequal scaling
For a slightly more complicated example, consider a sheet that is stretched unequally in two perpendicular directions along the coordinate axes, or, similarly, stretched in one direction, and shrunk in the other direction. In this case, there are two different scaling factors: k1 for the scaling in direction x, and k2 for the scaling in direction y. The transformation matrix is \begink_1 & 0\\0 & k_2\end, and the characteristic equation is (k_1-\lambda)(k_2-\lambda) = 0. The eigenvalues, obtained as roots of this equation are λ1 = k1, and λ2 = k2 which means, as expected, that the two eigenvalues are the scaling factors in the two directions. Plugging k1 back in the eigenvalue equation gives one of the eigenvectors:
- \begin0 & 0\\0 & k_2 - k_1\end \begin x \\ y\end = \begin0\\0\end or, more simply, y=0.
The figure shows the case where k_1>1 and 1>k_2>0. The rubber sheet is stretched along the x axis and simultaneously shrunk along the y axis. After repeatedly applying this transformation of stretching/shrinking many times, almost any vector on the surface of the rubber sheet will be oriented closer and closer to the direction of the x axis (the direction of stretching). The exceptions are vectors along the y-axis, which will gradually shrink away to nothing.
Example: Rotationdetails Rotation matrix A rotation in a plane is a transformation that describes motion of a vector, plane, coordinates, etc., around a fixed point. Clearly, for rotations other than through 0° and 180°, every vector in the real plane will have it's direction changed, and thus there cannot be any eigenvectors. But this is not necessarily if we consider the same matrix over a complex vector space.
A counterclockwise rotation in the horizontal plane about the origin at an angle φ is represented by the matrix
- \mathbf = \begin \cos \varphi & -\sin \varphi \\ \sin \varphi & \cos \varphi \end.
Rotation matrices on complex vector spacesThe characteristic equation has two complex roots λ1 and λ2. If we choose to think of the rotation matrix as a linear operator on the complex two dimensional, we can consider these complex eigenvalues. The roots are complex conjugates of each other: λ1,2 = cos φ ± i sin φ = e ± iφ, each with an algebraic multiplicity equal to 1, where i is the imaginary unit.
The first eigenvector is found by substituting the first eigenvalue, λ1, back in the eigenvalue equation:
- \begin \cos \varphi - \lambda_1 & -\sin \varphi \\ \sin \varphi & \cos \varphi - \lambda_1 \end \begin x \\ y \end = \begin - i \sin \varphi & -\sin \varphi \\ \sin \varphi & - i \sin \varphi \end \begin x \\ y \end = \begin 0 \\ 0 \end.
Infinite-dimensional spaces and Spectral Theorydetails Spectral theorem If the vector space is an infinite dimensional Banach space, the notion of eigenvalues can be generalized to the concept of spectrum. The spectrum is the set of scalars λ for which (T − λ)−1 is not defined; that is, such that T − λ has no bounded inverse.
Clearly if λ is an eigenvalue of T, λ is in the spectrum of T. In general, the converse is not true. There are operators on Hilbert or Banach spaces which have no eigenvectors at all. This can be seen in the following example. The bilateral shift on the Hilbert space ℓ 2(Z) (that is, the space of all sequences of scalars … a−1, a0, a1, a2, … such that
- \cdots + |a_|^2 + |a_0|^2 + |a_1|^2 + |a_2|^2 + \cdots
converges) has no eigenvalue but does have spectral values.
In infinite-dimensional spaces, the spectrum of a bounded operator is always nonempty. This is also true for an unbounded self adjoint operator. Via its spectral measures, the spectrum of any self adjoint operator, bounded or otherwise, can be decomposed into absolutely continuous, pure point, and singular parts. (See Decomposition of spectrum.)
The hydrogen atom is an example where both types of spectra appear. The eigenfunctions of the hydrogen atom Hamiltonian are called eigenstates and are grouped into two categories. The bound states of the hydrogen atom correspond to the discrete part of the spectrum (they have a discrete set of eigenvalues which can be computed by Rydberg formula) while the ionization processes are described by the continuous part (the energy of the collision/ionization is not quantified).
A common example of such maps on infinite dimensional spaces are the action of differential operators on function spaces. As an example, on the space of infinitely differentiable functions, the process of differentiation defines a linear operator since
- \displaystyle\frac(af+bg) = a \frac + b \frac,
The eigenvalue equation for linear differential operators is then a set of one or more differential equations. The eigenvectors are commonly called eigenfunctions. The most simple case is the eigenvalue equation for differentiation of a real valued function by a single real variable. In this case, the eigenvalue equation becomes the linear differential equation
- \displaystyle\frac f(x) = \lambda f(x).
- f(x) = A,
- f(x) = Ae^.
If we expand our horizons to complex valued functions, the value of λ can be any complex number. The spectrum of d/dt is therefore the whole complex plane. This is an example of a continuous spectrum.
Example: waves on a string
The displacement, h(x,t), of a stressed rope fixed at both ends, like the vibrating strings of a string instrument, satisfies the wave equation
- \frac = c^2\frac,
- X=-\fracX and T=-\omega^2 T.
- X = \sin(\frac + \phi) and T = \sin(\omega t + \psi).
- \sin(\phi) = 0, and so the phase angle \phi=0
- \sin(\frac) = 0,
- h(x,t) = \sin(n\pi x/L)\sin(\omega_n t).
EigendecompositionThe spectral theorem for matrices can be stated as follows. Let A be a square n × n matrix. Let q1 ... qk be an eigenvector basis, i.e. an indexed set of k linearly independent eigenvectors, where k is the dimension of the space spanned by the eigenvectors of A. If k = n, then A can be written
where Q is the square n × n matrix whose i-th column is the basis eigenvector qi of A and Λ is the diagonal matrix whose diagonal elements are the corresponding eigenvalues, i.e. Λii = λi.
An example of an eigenvalue equation where the transformation T is represented in terms of a differential operator is the time-independent Schrödinger equation in quantum mechanics:
- H\psi_E = E\psi_E \,
where H, the Hamiltonian, is a second-order differential operator and \psi_E, the wavefunction, is one of its eigenfunctions corresponding to the eigenvalue E, interpreted as its energy.
However, in the case where one is interested only in the bound state solutions of the Schrödinger equation, one looks for \psi_E within the space of square integrable functions. Since this space is a Hilbert space with a well-defined scalar product, one can introduce a basis set in which \psi_E and H can be represented as a one-dimensional array and a matrix respectively. This allows one to represent the Schrödinger equation in a matrix form. (Fig. 8 presents the lowest eigenfunctions of the Hydrogen atom Hamiltonian.)
The Dirac notation is often used in this context. A vector, which represents a state of the system, in the Hilbert space of square integrable functions is represented by |\Psi_E\rangle. In this notation, the Schrödinger equation is:
- H|\Psi_E\rangle = E|\Psi_E\rangle
where |\Psi_E\rangle is an eigenstate of H. It is a self adjoint operator, the infinite dimensional analog of Hermitian matrices (see Observable). As in the matrix case, in the equation above H|\Psi_E\rangle is understood to be the vector obtained by application of the transformation H to |\Psi_E\rangle.
In quantum mechanics, and in particular in atomic and molecular physics, within the Hartree-Fock theory, the atomic and molecular orbitals can be defined by the eigenvectors of the Fock operator. The corresponding eigenvalues are interpreted as ionization potentials via Koopmans' theorem. In this case, the term eigenvector is used in a somewhat more general meaning, since the Fock operator is explicitly dependent on the orbitals and their eigenvalues. If one wants to underline this aspect one speaks of nonlinear eigenvalue problem. Such equations are usually solved by an iteration procedure, called in this case self-consistent field method. In quantum chemistry, one often represents the Hartree-Fock equation in a non-orthogonal basis set. This particular representation is a generalized eigenvalue problem called Roothaan equations.
Geology and glaciology: (orientation tensor)
In geology, especially in the study of glacial till, eigenvectors and eigenvalues are used as a method by which a mass of information of a clast fabric's constituents' orientation and dip can be summarized in a 3-D space by six numbers. In the field, a geologist may collect such data for hundreds or thousands of clasts in a soil sample, which can only be compared graphically such as in a Tri-Plot (Sneed and Folk) diagram , , or as a Stereonet on a Wulff Net . The output for the orientation tensor is in the three orthogonal (perpendicular) axes of space. Eigenvectors output from programs such as Stereo32 are in the order E1 ≥ E2 ≥ E3, with E1 being the primary orientation of clast orientation/dip, E2 being the secondary and E3 being the tertiary, in terms of strength. The clast orientation is defined as the eigenvector, on a compass rose of 360°. Dip is measured as the eigenvalue, the modulus of the tensor: this is valued from 0° (no dip) to 90° (vertical). The relative values of E1, E2, and E3 are dictated by the nature of the sediment's fabric. If E1 = E2 = E3, the fabric is said to be isotropic. If E1 = E2 > E3 the fabric is planar. If E1 > E2 > E3 the fabric is linear. See 'A Practical Guide to the Study of Glacial Sediments' by Benn & Evans, 2004 .
In factor analysis, the eigenvectors of a covariance matrix or correlation matrix correspond to factors, and eigenvalues to the variance explained by these factors. Factor analysis is a statistical technique used in the social sciences and in marketing, product management, operations research, and other applied sciences that deal with large quantities of data. The objective is to explain most of the covariability among a number of observable random variables in terms of a smaller number of unobservable latent variables called factors. The observable random variables are modeled as linear combinations of the factors, plus unique variance terms. Eigenvalues are used in analysis used by Q-methodology software; factors with eigenvalues greater than 1.00 are considered significant, explaining an important amount of the variability in the data, while eigenvalues less than 1.00 are considered too weak, not explaining a significant portion of the data variability.
EigenfacesIn image processing, processed images of faces can be seen as vectors whose components are the brightnesses of each pixel. The dimension of this vector space is the number of pixels. The eigenvectors of the covariance matrix associated to a large set of normalized pictures of faces are called eigenfaces; this is an example of principal components analysis. They are very useful for expressing any face image as a linear combination of some of them. In the facial recognition branch of biometrics, eigenfaces provide a means of applying data compression to faces for identification purposes. Research related to eigen vision systems determining hand gestures has also been made. More on determining sign language letters using eigen systems can be found here: http://www.geigel.com/signlanguage/index.php
Similar to this concept, eigenvoices concept is also developed which represents the general direction of variability in human pronunciations of a particular utterance, such as a word in a language. Based on a linear combination of such eigenvoices, a new voice pronunciation of the word can be constructed. These concepts have been found useful in automatic speech recognition systems, for speaker adaptation.
Tensor of inertia
In mechanics, the eigenvectors of the inertia tensor define the principal axes of a rigid body. The tensor of inertia is a key quantity required in order to determine the rotation of a rigid body around its center of mass.
In solid mechanics, the stress tensor is symmetric and so can be decomposed into a diagonal tensor with the eigenvalues on the diagonal and eigenvectors as a basis. Because it is diagonal, in this orientation, the stress tensor has no shear components; the components it does have are the principal components.
Eigenvalues of a graph
In spectral graph theory, an eigenvalue of a graph is defined as an eigenvalue of the graph's adjacency matrix A, or (increasingly) of the graph's Laplacian matrix, which is either T−A or I−T 1/2AT −1/2, where T is a diagonal matrix holding the degree of each vertex, and in T −1/2, 0 is substituted for 0−1/2. The kth principal eigenvector of a graph is defined as either the eigenvector corresponding to the kth largest eigenvalue of A, or the eigenvector corresponding to the kth smallest eigenvalue of the Laplacian. The first principal eigenvector of the graph is also referred to merely as the principal eigenvector.
The principal eigenvector is used to measure the centrality of its vertices. An example is Google's PageRank algorithm. The principal eigenvector of a modified adjacency matrix of the World Wide Web graph gives the page ranks as its components. This vector corresponds to the stationary distribution of the Markov chain represented by the row-normalized adjacency matrix; however, the adjacency matrix must first be modified to ensure a stationary distribution exists. The second principal eigenvector can be used to partition the graph into clusters, via spectral clustering. Other methods are also available for clustering.
- Pigolkina, T. S. and Shulman, V. S., Eigenvalue (in Russian), In:Vinogradov, I. M. (Ed.), Mathematical Encyclopedia, Vol. 5, Soviet Encyclopedia, Moscow, 1977.
- Pigolkina, T. S. and Shulman, V. S., Eigenvector (in Russian), In:Vinogradov, I. M. (Ed.), Mathematical Encyclopedia, Vol. 5, Soviet Encyclopedia, Moscow, 1977.
- Curtis, Charles W., Linear Algebra: An Introductory Approach, 347 p., Springer; 4th ed. 1984. Corr. 7th printing edition (August 19, 1999), ISBN 0387909923.
- MIT Video Lecture on Eigenvalues and Eigenvectors at Google Video, from MIT OpenCourseWare
- ARPACK is a collection of FORTRAN subroutines for solving large scale (sparse) eigenproblems.
- IRBLEIGS, has MATLAB code with similar capabilities to ARPACK. (See this paper for a comparison between IRBLEIGS and ARPACK.)
- LAPACK is a collection of FORTRAN subroutines for solving dense linear algebra problems
- ALGLIB includes a partial port of the LAPACK to C++, C#, Delphi, etc.
- MathWorld: Eigenvector
- Online calculator for Eigenvalues and Eigenvectors
- Online Matrix Calculator Calculates eigenvalues, eigenvectors and other decompositions of matrices online
- Vanderplaats Research and Development - Provides the SMS eigenvalue solver for Structural Finite Element. The solver is in the GENESIS program as well as other commercial programs. SMS can be easily use with MSC.Nastran or NX/Nastran via DMAPs.
- What are Eigen Values? from PhysLink.com's "Ask the Experts"
- Templates for the Solution of Algebraic Eigenvalue Problems Edited by Zhaojun Bai, James Demmel, Jack Dongarra, Axel Ruhe, and Henk van der Vorst (a guide to the numerical solution of eigenvalue problems)
eigenvectors in Arabic: قيمة ذاتية
eigenvectors in Belarusian (Tarashkevitsa): Уласныя лікі, вэктары й прасторы
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